Identification of Key Functional Gene Signatures Indicative of Dedifferentiation in Papillary Thyroid Cancer - Xu, Weibo; Li, Cuiwei; Ma, Ben; Lu, Zhongwu; Wang, Yuchen; Jiang, Hongyi; Luo, Yi; Yang, Yichen; Wang, Xiao; Liao, Tian; Ji, Qinghai; Wang, Yu; et al. (Frontiers in Oncology, 2021)
Background: Differentiated thyroid cancer (DTC) is the most common type of thyroid cancers. Many of them can relapse to dedifferentiated thyroid cancer (DDTC) and exhibit different gene expression profiles. The underlying mechanism of dedifferentiation and the involved genes or pathways remained to be investigated. Methods: A discovery cohort obtained from patients who received surgical resection in the FUSCC and two validation cohorts derived from The Gene Expression Omnibus (GEO) database were used to screen out differentially expressed genes in dedifferentiation process. Weighted gene co-expression network analysis (WGCNA) was constructed to identify modules highly related to differentiation. Gene Set Enrichment Analysis (GSEA) was used to identify pathways related to differentiation, and all differentially expressed genes were grouped by function based on the GSEA and literature reviewing data. Least absolute shrinkage and selection operator (LASSO) regression was used to control the number of variables in each group. Next, we used logistic regression to build a gene signature in each group to indicate differentiation and we computed receiver operating characteristic (ROC) curve to evaluate the indicative performance of each signature. Results: A total of 307 upregulated and 313 downregulated genes in poorly differentiated thyroid cancer (PDTC) compared with PTC and normal thyroid (NT) were screened out in FUSCC cohort and validated in two GEO cohorts. WGCNA analysis of 620 differential genes yielded the seven core genes with the highest correlation with thyroid differentiation score (TDS). Furthermore, 395 genes significantly correlated with TDS in univariate logistic regression analysis were divided into 11 groups. The areas under the ROC curve (AUC) of gene signature of group transcription and epigenetic modification, signal and substance transport, extracellular matrix , and metabolism in the training set (TCGA cohort) and validation set (combined GEO cohort) were both greater than 0.75. Gene signature based on group transcription and epigenetic modification, cilia formation and movement, and proliferation can reflect the patient’s disease recurrence state. Conclusion: The dedifferentiation of DTC is affected by a variety of mechanisms including many genes. Gene signature of group transcription and epigenetic modification, signal and substance transport, ECM, and metabolism can be used as biomarkers for DDTC.